Claude Code Collaboration

Integrates OpenClaw with Claude Code CLI to delegate coding tasks via JSON queues for advanced multi-turn code collaboration and analysis.

Enable OpenClaw to delegate tasks to Claude Code CLI for collaborative discussions and complex task execution. When OpenClaw needs deeper analysis, coding assistance, or multi-turn discussions with a powerful code-generation model, it writes tasks to a queue and Claude Code Interface Agent executes them.

Architecture

text

OpenClaw (main agent)
  ↓ writes task .json
Claude Code Interface Agent (polling loop)
  ↓ calls claude CLI with env vars
Claude Code CLI →阿里云 API (qwen3.5-plus)
  ↓ returns result .json
OpenClaw reads result and continues

Setup Requirements

1. Install Claude Code CLI

bash

# macOS
brew install anthropic/claude-code/claude-code

# Or via npm
npm install -g @anthropic/claude-code

2. Configure Environment Variables

The agent requires these environment variables:

bash

export ANTHROPIC_AUTH_TOKEN="your-api-token"
export ANTHROPIC_BASE_URL="https://coding.dashscope.aliyuncs.com/apps/anthropic"
export ANTHROPIC_MODEL="qwen3.5-plus"  # or your preferred model

3. Start the Interface Agent

bash

mkdir -p ~/.openclaw/agents/main/workspace/.oc-cc-in
mkdir -p ~/.openclaw/agents/main/workspace/.oc-cc-out

python3 agent.py &

Usage

Sending a Task

Create a JSON file in .oc-cc-in/ with:

json

{
  "task_id": "unique-task-id",
  "prompt": "Your question or task for Claude Code",
  "priority": "normal"
}

The interface agent polls the directory, executes via Claude Code CLI, and writes results to .oc-cc-out/{task_id}.json.

Reading Results

Results are JSON files with:

• task_id: Task identifier
• status: "completed" or "failed"
• prompt: Original prompt
• stdout: Claude Code's response
• stderr: Error output if any
• returncode: Exit code
• completed_at: ISO timestamp

Example: Collaborative Discussion

OpenClaw sends:

json

{
  "task_id": "discuss-01",
  "prompt": "Analyze the pros and cons of microservices vs monolith for a small startup. Provide concrete recommendations.",
  "priority": "high"
}

Claude Code returns detailed analysis in stdout, OpenClaw reads it and continues the conversation with the user.

Directory Structure

text

.oc-cc-agent/
├── agent.py          # Interface agent (polling loop)
.oc-cc-in/            # Input queue (tasks to process)
.oc-cc-out/           # Output queue (results)
.oc-cc-chat.log      # Conversation log
.oc-cc-status.json   # Agent status

Monitoring

• Status: Check .oc-cc-status.json for agent state
• Logs: Conversation log at .oc-cc-chat.log
• View Results: Read JSON files in .oc-cc-out/

Configuration

Key settings in agent.py:

python

WORK_DIR = "/path/to/workspace"  # Claude Code's working directory
IN_DIR = f"{WORK_DIR}/.oc-cc-in"
OUT_DIR = f"{WORK_DIR}/.oc-cc-out"
LOG_FILE = f"{WORK_DIR}/.oc-cc-chat.log"
PORT = 18790  # Optional HTTP server port
TIMEOUT = 120  # seconds